library(DESeq2)
library(ggplot2)
library(reshape2)
library(patchwork)
library(ggrepel)
library(cowplot)
library(grid)
library(RColorBrewer)
library(repr) 
library(glmpca)
library(pheatmap)
library(PoiClaClu)
library(apeglm)
library(ashr)
library(vsn)
library(dplyr)
library(tidyr)
library(viridis)  
library("pheatmap")
library("ReportingTools")
library("BiocParallel")
library(glmpca)
library(emdbook)  
library(tidyverse)
register(MulticoreParam(4))
library(sva)
library(RUVSeq)
library(GenomicRanges)
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(org.Hs.eg.db)

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ChIPseeker v1.42.1 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Qianwen Wang, Ming Li, Tianzhi Wu, Li Zhan, Lin Li, Meijun Chen, Wenqin
Xie, Zijing Xie, Erqiang Hu, Shuangbin Xu, Guangchuang Yu. Exploring
epigenomic datasets by ChIPseeker. Current Protocols. 2022, 2(10): e585

Loading required package: GenomicFeatures

Loading required package: AnnotationDbi


Attaching package: 'AnnotationDbi'


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    select

# Load count data
count_data <- read.csv("A549_peak_counts.txt", 
                       skip = 1, sep="\t", header=TRUE, stringsAsFactors=FALSE)  

head(count_data,2 )

# Remove the ".genomicAllAligned.sorted.bam" suffix from column names
colnames(count_data) <- sub("\\.CLAM.genomicAllAligned\\.sorted\\.bam$", "", colnames(count_data))
head(count_data, 2)
colnames(count_data)

colnames(count_data) <- sub("^([^\\.]+)\\..*", "\\1", colnames(count_data))
head(count_data, 2)
colnames(count_data, 2)

count_data2 <- count_data %>%
  mutate(
    Chr = sapply(strsplit(Chr, ";"), `[`, 1),
    Start = sapply(strsplit(Start, ";"), function(x) min(as.integer(x))),
    End = sapply(strsplit(End, ";"), function(x) max(as.integer(x)))
  )

colnames(count_data2)
head(count_data2, 2)

colnames(count_data2)
head(count_data2, 18)

print("number of peaks:")
dim(count_data2)[1]

# Convert to GRanges object
gr <- GRanges(
  seqnames = count_data2$Chr,
  ranges = IRanges(start = count_data2$Start, end = count_data2$End)
)

# Merge overlapping intervals
gr_merge <- GenomicRanges::reduce(gr)

# Convert back to data frame
df_merge <- as.data.frame(gr_merge)[, c("seqnames", "start", "end")]
colnames(df_merge) <- c("Chr", "Start", "End")

# Print or return merged peaks
dim(df_merge)[1]

# Annotate merged peaks to genome features
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

peak_anno <- annotatePeak(gr_merge, 
                          TxDb = txdb, 
                          tssRegion = c(-3000, 3000), 
                          annoDb = "org.Hs.eg.db")

# 📊 Plot annotation breakdown 
print("the annotation of the peaks:")
plotAnnoPie(peak_anno)

[1] "number of peaks:"

>> preparing features information...		 2025-04-18 04:01:03 PM 
>> identifying nearest features...		 2025-04-18 04:01:05 PM 
>> calculating distance from peak to TSS...	 2025-04-18 04:01:05 PM 
>> assigning genomic annotation...		 2025-04-18 04:01:05 PM 
>> adding gene annotation...			 2025-04-18 04:01:25 PM

'select()' returned 1:many mapping between keys and columns

>> assigning chromosome lengths			 2025-04-18 04:01:25 PM 
>> done...					 2025-04-18 04:01:25 PM 
[1] "the annotation of the peaks:"

# Select _IP columns + Geneid
ip <- count_data2 %>% dplyr::select(Geneid, ends_with("_IP"))
head(ip, 2)

# Select _NP columns + Geneid
np <- count_data2 %>% dplyr::select(Geneid, ends_with("_NP"))
head(np, 2)

print("DESeq2 to measure the differential binding across IP samples :")

[1] "DESeq2 to measure the differential binding across IP samples :"

# Step 1: Select only columns ending with "_IP" and include Geneid for rownames
ip <- count_data2 %>%
     dplyr::select(Geneid, ends_with("_IP"))

# Step 2: Set Geneid as rownames and remove Geneid column
rownames(ip) <- ip$Geneid
ip <- ip[, -1]

# Step 3: Verify column names
sample_names <- colnames(ip)
print(sample_names)

# Step 4: Assign condition groups (AC, AP, AV)
conditions <- ifelse(grepl("^AC", sample_names), "AC",
              ifelse(grepl("^AP", sample_names), "AP",
              ifelse(grepl("^AV", sample_names), "AV", NA)))
print(conditions)

# Step 5: Error handling if any unrecognized sample names
if (any(is.na(conditions))) {
  stop("Some sample names do not match expected patterns (AC, AP, AV). Check column names!")
}

# Step 6: Create colData
col_data <- data.frame(
  row.names = sample_names,
  condition = factor(conditions, levels = c("AC", "AP", "AV"))
)
print("col data:")
print(col_data)

# Step 7: Remove rows with NA values
dim(ip)
ip <- ip[complete.cases(ip), ]
dim(ip)

# Step 8: Compute summary statistics directly on ip (only numeric columns)
summary_stats <- data.frame(
  Median = apply(ip, 2, median, na.rm = TRUE),
  Min    = apply(ip, 2, min, na.rm = TRUE),
  Max    = apply(ip, 2, max, na.rm = TRUE)
)

# Step 9: Print the result
print(summary_stats)

[1] "AC1_IP" "AC2_IP" "AP1_IP" "AP2_IP" "AV1_IP" "AV2_IP"
[1] "AC" "AC" "AP" "AP" "AV" "AV"
[1] "col data:"
       condition
AC1_IP        AC
AC2_IP        AC
AP1_IP        AP
AP2_IP        AP
AV1_IP        AV
AV2_IP        AV

       Median Min    Max
AC1_IP  179.0   0 588918
AC2_IP  240.5   0 606823
AP1_IP  187.0   0 504829
AP2_IP  218.0   0 691186
AV1_IP   55.0   0 151888
AV2_IP   60.0   0 157687

# Create DESeq2 dataset
dds <- DESeqDataSetFromMatrix(countData = ip, colData = col_data, design = ~condition)

# Set reference level for condition (AV will be the baseline)
dds$condition <- relevel(dds$condition, ref = "AV")

# Print number of genes before filtering
cat("Number of genes before filtering:", nrow(dds), "\n")

# Estimate size factors (required for normalization)
dds <- estimateSizeFactors(dds)

# Filter: keep genes with normalized count >= 4 in at least 4 samples
keep <- rowSums(counts(dds, normalized = TRUE) >= 4) >= 4
dds <- dds[keep, ]

# Print number of genes after filtering
cat("Number of genes after filtering:", nrow(dds), "\n")

# Run DESeq2 differential expression analysis
dds <- DESeq(dds)

# Extract results table
# res <- results(dds) 
# it will produce : Wald test p-value: condition AC vs AV 

# View summary of results
# cat("First row of DE results:\n")
# print(head(res, 1))
# cat("Last row of DE results:\n")
# print(tail(res, 1))
# cat("Summary of DESeq2 results:\n")
# print(summary(res))

# Show contrast names
cat("Available result contrasts:\n")
print(resultsNames(dds))

# Size factors (re-estimation here is harmless but redundant)
cat("The size factors are:\n")
print(sizeFactors(dds))

# Extract normalized counts
norm_counts <- counts(dds, normalized = TRUE)

# Preview normalized counts
cat("Preview of normalized counts:\n")
print(head(norm_counts, 2))

# Save normalized counts to CSV
write.csv(norm_counts, "A549.peaks.IP.samples.normalized.counts.csv", row.names = TRUE)

# Compute summary statistics for each sample (column)
summary_stats2 <- data.frame(
  Median = apply(norm_counts, 2, median, na.rm = TRUE),
  Min    = apply(norm_counts, 2, min, na.rm = TRUE),
  Max    = apply(norm_counts, 2, max, na.rm = TRUE)
)

# Print summary statistics
cat("Summary of the normalized counts:\n")
print(round(summary_stats2, 2))

# Show available assays in the DESeqDataSet
cat("Available assays in dds:\n")
print(names(assays(dds)))

# View fitted means (mu) and Cook's distances
cat("DESeq2 fitted means (mu):\n")
print(head(assay(dds, "mu"), 2))

cat("DESeq2 Cook's distances (cooks):\n")
print(head(assay(dds, "cooks"), 2))

Number of genes before filtering: 11828 
Number of genes after filtering: 10263

using pre-existing size factors

estimating dispersions

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

Available result contrasts:
[1] "Intercept"          "condition_AC_vs_AV" "condition_AP_vs_AV"
The size factors are:
   AC1_IP    AC2_IP    AP1_IP    AP2_IP    AV1_IP    AV2_IP 
1.3569838 1.7806372 1.4003870 1.6497101 0.4226442 0.4513182 
Preview of normalized counts:
                  AC1_IP   AC2_IP   AP1_IP   AP2_IP    AV1_IP   AV2_IP
ENSG00000310528 11.05393 15.72471 14.99586 12.72951  4.732113 11.07866
ENSG00000310527 58.21735 48.29732 88.54695 67.89072 30.758732 31.02024
Summary of the normalized counts:
       Median Min      Max
AC1_IP 185.71   0 433990.5
AC2_IP 187.01   0 340789.8
AP1_IP 182.09   0 360492.5
AP2_IP 186.70   0 418974.2
AV1_IP 182.19   0 359375.6
AV2_IP 186.12   0 349392.1
Available assays in dds:
[1] "counts" "mu"     "H"      "cooks" 
DESeq2 fitted means (mu):
                  AC1_IP   AC2_IP    AP1_IP    AP2_IP   AV1_IP    AV2_IP
ENSG00000310528 18.24995 23.94762  19.38885  22.84082  3.36674  3.595154
ENSG00000310527 72.11762 94.63291 109.39270 128.86884 13.05625 13.942046
DESeq2 Cook's distances (cooks):
                    AC1_IP     AC2_IP     AP1_IP     AP2_IP       AV1_IP
ENSG00000310528 0.17580020 0.20951993 0.04063713 0.04515130 2.934290e-01
ENSG00000310527 0.02695118 0.02864283 0.05604267 0.05755543 4.290547e-05
                      AV2_IP
ENSG00000310528 3.220225e-01
ENSG00000310527 4.529699e-05

# Extract raw (unnormalized) counts
print("raw counts")
raw_counts <- counts(dds, normalized = FALSE)
head(raw_counts, 2)

# Extract normalized counts
print("norm counts")
norm_counts <- counts(dds, normalized = TRUE)
head(norm_counts, 2)

[1] "raw counts"

[1] "norm counts"

print("Boxplot of Raw vs log2 Normalized Counts")

# Prepare raw counts
raw_counts <- as.data.frame(counts(dds, normalized = FALSE))
raw_counts$Gene <- rownames(raw_counts)
raw_long <- pivot_longer(raw_counts, -Gene, names_to = "Sample", values_to = "Count")
raw_long$log2_count <- log2(raw_long$Count + 1)

# Prepare normalized counts
norm_counts <- as.data.frame(counts(dds, normalized = TRUE))
norm_counts$Gene <- rownames(norm_counts)
norm_long <- pivot_longer(norm_counts, -Gene, names_to = "Sample", values_to = "Count")
norm_long$log2_count <- log2(norm_long$Count + 1)

# Color palette
sample_list <- unique(c(raw_long$Sample, norm_long$Sample))
sample_colors <- setNames(viridis::viridis(length(sample_list), option = "D"), sample_list)

# Plot p1: Raw counts
p1 <- ggplot(raw_long, aes(x = Sample, y = log2_count, fill = Sample)) +
  geom_boxplot(outlier.size = 0.5, width = 0.7) +
  scale_fill_manual(values = sample_colors, name = "Sample") +
  theme_minimal(base_size = 12) +
  labs(title = "Raw Counts (log2 scale)",
       y = "log2(Counts + 1)", x = "Sample") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Plot p2: Normalized counts
p2 <- ggplot(norm_long, aes(x = Sample, y = log2_count, fill = Sample)) +
  geom_boxplot(outlier.size = 0.5, width = 0.7) +
  scale_fill_manual(values = sample_colors, name = "Sample") +
  theme_minimal(base_size = 12) +
  labs(title = "Normalized Counts (log2 scale)",
       y = "log2(Counts + 1)", x = "Sample") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')

[1] "Boxplot of Raw vs log2 Normalized Counts"

# Extract raw counts and log-transform
raw_counts <- counts(dds, normalized = FALSE)
raw_log_counts <- log10(raw_counts + 1)

# Convert to long format for ggplot2
log1_df <- as.data.frame(raw_log_counts)
log1_df$Gene <- rownames(log1_df)
log1_long <- pivot_longer(log1_df, -Gene, names_to = "Sample", values_to = "log10_count")

# Extract normalized counts and log-transform
norm_counts <- counts(dds, normalized = TRUE)
norm_log_counts <- log10(norm_counts + 1)

# Convert to long format for ggplot2
log2_df <- as.data.frame(norm_log_counts)
log2_df$Gene <- rownames(log2_df)
log2_long <- pivot_longer(log2_df, -Gene, names_to = "Sample", values_to = "log10_count")

# Plot with ggplot2
p1 = ggplot(log1_long, aes(x = log10_count, color = Sample)) +
  geom_density(size = 1.2, alpha = 0.8) +
  theme_minimal(base_size = 14) +
  labs(
    title = "Density Plot of log10 Raw Counts (Before Normalization)",
    x = "log10(Counts + 1)",
    y = "Density"
  ) +
  theme(
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10),
    legend.position = "right"
  )
# Plot with ggplot2
p2 = ggplot(log2_long, aes(x = log10_count, color = Sample)) +
  geom_density(size = 1.2, alpha = 0.8) +
  theme_minimal(base_size = 14) +
  labs(
    title = "Density Plot of log10 Normalized Counts",
    x = "log10(Normalized Counts + 1)",
    y = "Density"
  ) +
  theme(
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10),
    legend.position = "right"
  )

# Combine side-by-side
options(repr.plot.width = 14, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2, align = 'h')

Warning message:
"Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead."

print("printing the results")
resultsNames(dds)

[1] "printing the results"

print("No shrinkage")

# Get results for different comparisons
res_AP_vs_AC <- results(dds, contrast = c("condition", "AP", "AC"))
res_AP_vs_AV <- results(dds, contrast = c("condition", "AP", "AV"))
res_AC_vs_AV <- results(dds, contrast = c("condition", "AC", "AV"))

# summary(res_AP_vs_AC)
# summary(res_AC_vs_AV)
# summary(res_AP_vs_AV)

# Save results
write.csv(as.data.frame(res_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.csv")
write.csv(as.data.frame(res_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.csv")
write.csv(as.data.frame(res_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.csv")

###########################################################
###########################################################

print("number of differentially bound transcripts : AP vs AC : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(res_AP_vs_AC, padj < 0.1))[1]

print("number of differentially bound transcripts : AP vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(res_AP_vs_AV, padj < 0.1))[1]

print("number of differentially bound transcripts : AC vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(res_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(res_AC_vs_AV, padj < 0.1))[1]

###########################################################
###########################################################

[1] "No shrinkage"
[1] "number of differentially bound transcripts : AP vs AC : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound transcripts : AP vs AV : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound transcripts : AC vs AV : pvalue < 0.05 and padj < 0.1"

# type = c("apeglm", "ashr", "normal")

print("Data shrinkage : normal lfcShrink")

[1] "Data shrinkage : normal lfcShrink"

# Get results for different comparisons

resLFCnormal_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="normal")
resLFCnormal_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="normal")
resLFCnormal_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="normal")

# summary(resLFCnormal_AP_vs_AC)
# summary(resLFCnormal_AC_vs_AV)
# summary(resLFCnormal_AP_vs_AV)

# Save results
write.csv(as.data.frame(resLFCnormal_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.resLFCnormal.csv")
write.csv(as.data.frame(resLFCnormal_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.resLFCnormal.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCnormal: AP vs AC : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AP_vs_AC, pvalue < 0.05))
dim(subset(resLFCnormal_AP_vs_AC, padj < 0.1))

print("number of differentially bound and expressed transcripts : resLFCnormal : AP vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AP_vs_AV, pvalue < 0.05))
dim(subset(resLFCnormal_AP_vs_AV, padj < 0.1))

print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 0.1")
dim(subset(resLFCnormal_AC_vs_AV, pvalue < 0.05))
dim(subset(resLFCnormal_AC_vs_AV, padj < 0.1))

###########################################################
###########################################################

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).

Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895

[1] "number of differentially bound and expressed transcripts : resLFCnormal: AP vs AC : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : resLFCnormal : AP vs AV : pvalue < 0.05 and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 0.1"

print("Data shrinkage : ashr lfcShrink")

[1] "Data shrinkage : ashr lfcShrink"

# If you must use contrast, you should use type="normal" or type="ashr" instead of apeglm, 
# because apeglm only works with coef.  
# Apeglm is the recommended method for log-fold change shrinkage.

# Get results for different comparisons
# resLFCapeglm_AP_vs_AV <- lfcShrink(dds, coef = "condition_AP_vs_AV", type="apeglm")
# resLFCapeglm_AC_vs_AV <- lfcShrink(dds, coef = "condition_AC_vs_AV", type="apeglm")

resLFCashr_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="ashr")
resLFCashr_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="ashr")
resLFCashr_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="ashr")

# summary(resLFCashr_AP_vs_AC)
# summary(resLFCashr_AC_vs_AV)
# summary(resLFCashr_AP_vs_AV)

# Save results
write.csv(as.data.frame(resLFCashr_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.resLFCashr.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AC, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 ")
dim(subset(resLFCashr_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AV, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05")
dim(subset(resLFCashr_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AC_vs_AV, padj < 0.1))[1]

###########################################################
###########################################################

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 "

[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05"

# If you must use contrast, you should use type="normal" or type="ashr" instead of apeglm, 
# because apeglm only works with coef.  
# Apeglm is the recommended method for log-fold change shrinkage.

# Get results for different comparisons
# resLFCapeglm_AP_vs_AV <- lfcShrink(dds, coef = "condition_AP_vs_AV", type="apeglm")
# resLFCapeglm_AC_vs_AV <- lfcShrink(dds, coef = "condition_AC_vs_AV", type="apeglm")

resLFCashr_AP_vs_AV <- lfcShrink(dds, contrast = c("condition", "AP", "AV"), type="ashr")
resLFCashr_AC_vs_AV <- lfcShrink(dds, contrast = c("condition", "AC", "AV"), type="ashr")
resLFCashr_AP_vs_AC <- lfcShrink(dds, contrast = c("condition", "AP", "AC"), type="ashr")

# summary(resLFCashr_AP_vs_AC)
# summary(resLFCashr_AC_vs_AV)
# summary(resLFCashr_AP_vs_AV)

# Save results
write.csv(as.data.frame(resLFCashr_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.resLFCashr.csv")
write.csv(as.data.frame(resLFCashr_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.resLFCashr.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AC, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AC, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AP_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AP_vs_AV, padj < 0.1))[1]

print("number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 1")
dim(subset(resLFCashr_AC_vs_AV, pvalue < 0.05))[1]
dim(subset(resLFCashr_AC_vs_AV, padj < 0.1))[1]

###########################################################
###########################################################

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AC : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : AP vs AV : pvalue < 0.05 and padj < 1"

[1] "number of differentially bound and expressed transcripts : resLFCashr : AC vs AV : pvalue < 0.05 and padj < 1"

print("Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05")

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

# The information about DE peaks was stored in :
# res_AP_vs_AC 
# res_AP_vs_AV 
# res_AC_vs_AV 

# resLFCnormal_AP_vs_AV
# resLFCnormal_AC_vs_AV 
# resLFCnormal_AP_vs_AC 

# resLFCashr_AP_vs_AV 
# resLFCashr_AC_vs_AV 
# resLFCashr_AP_vs_AC 

# Count DEGs
n_DE_unshrunken <- sum(res_AP_vs_AC$pvalue < 0.05, na.rm = TRUE)
n_DE_shrink_normal <- sum(resLFCnormal_AP_vs_AC$pvalue < 0.05, na.rm = TRUE)  # Same p-values as unshrunken
n_DE_ashr <- sum( resLFCashr_AP_vs_AC$pvalue < 0.05, na.rm = TRUE)            # Same p-values as unshrunken

# Build a data frame
compare_df1 <- data.frame(
  Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
  DE_Genes = c(n_DE_unshrunken, n_DE_shrink_normal, n_DE_ashr)
)

# Plot it
p1 = ggplot(compare_df1, aes(x = Method, y = DE_Genes, fill = Method)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(
    title = "Comparison of DE Gene Counts (AC vs AP)",
    y = "Number of DE Genes (p < 0.05 or s < 0.05)",
    x = "Shrinkage Method"
  ) +
  theme(legend.position = "none") +
  geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4)

print("Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05")

# Shrinkage Methods: The lfcShrink() function in DESeq2 is used to obtain more accurate estimates of log2 fold changes, 
# especially for genes with low counts or high variability.
# Threshold Selection: The choice of a log2FC threshold (e.g., 0.3) is somewhat arbitrary and should be based on the biological context 
# and the desired stringency of the analysis.
# Interpretation: Comparing the number of DE genes across different shrinkage methods can provide insights into the robustness of your findings. 
# It's common to observe variations in the number of DE genes identified, depending on the method used.

# Raw (non-shrunk)
n_raw <- sum(res_AP_vs_AC$pvalue < pval_cutoff & abs(res_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Normal shrink
n_normal <- sum(res_AP_vs_AC$pvalue < pval_cutoff & abs(resLFCnormal_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)
# Ashr shrink (using s-value instead of p-value)
n_ashr <- sum(resLFCashr_AP_vs_AC$pvalue < pval_cutoff & abs(resLFCashr_AP_vs_AC$log2FoldChange) > lfc_cutoff, na.rm = TRUE)

# Combine into a data frame
compare_df2 <- data.frame(
  Method = c("Unshrunken", "Shrink: normal", "Shrink: ashr"),
  DE_Genes = c(n_raw, n_normal, n_ashr)
)

p2 = ggplot(compare_df2, aes(x = Method, y = DE_Genes, fill = Method)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(
    title = "DE Gene Counts (AP vs AC)",
    subtitle = "p < 0.05 and |log2FC| > 0.5",
    y = "Number of DE Genes",
    x = "Method"
  ) +
  geom_text(aes(label = DE_Genes), vjust = -0.3, size = 4) +
  theme(legend.position = "none")


# Print the plot in Jupyter
options(repr.plot.width = 8, repr.plot.height = 6)
p1 + p2

[1] "Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05"
[1] "Comparing the number of DE genes for the comparison : AP vs AC for a pvalue < 0.05"

print("MA plots:")

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

[1] "MA plots:"

make_MA_plot <- function(res_df, title = "MA Plot", lfc_cutoff = 0.3, pval_cutoff = 0.1, ylim = c(-2, 2)) {
  
  res_df <- as.data.frame(res_df)
  
  # Replace NA p-values with the threshold so they are not considered significant
  res_df$pvalue[is.na(res_df$pvalue)] <- 1
  
  # Label significance based on thresholds
  res_df$sig <- ifelse(res_df$pvalue < pval_cutoff & abs(res_df$log2FoldChange) > lfc_cutoff,
                       "Significant", "Not Significant")

  # Generate the MA plot
  ggplot(res_df, aes(x = baseMean, y = log2FoldChange, color = sig)) +
    geom_point(alpha = 0.6, size = 1) +
    scale_x_log10() +
    scale_color_manual(values = c("Significant" = "#D7263D", "Not Significant" = "gray70")) +
    geom_hline(yintercept = c(-lfc_cutoff, lfc_cutoff), linetype = "dashed", color = "black") +
    coord_cartesian(ylim = ylim) +
    theme_minimal(base_size = 14) +
    labs(
      title = title,
      x = "Mean Expression (log10 scale)",
      y = "log2 Fold Change",
      color = "Significance"
    ) +
    theme(
      legend.position = "right",
      panel.grid.minor = element_blank()
    )
}

print("MA plots:")
print("AP vs AC")

# Create the plots
p1 <- make_MA_plot(res_AP_vs_AC, title = "AP vs AC (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AP_vs_AC, title = "AP vs AC (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AP_vs_AC, title = "AP vs AC (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "AP vs AC"

print("MA plots:")
print("AC vs AV")

# Create the plots
p1 <- make_MA_plot(res_AC_vs_AV, title = "AC vs AV (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AC_vs_AV, title = "AC vs AV (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AC_vs_AV, title = "AC vs AV (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "AC vs AV"

print("MA plots:")
print("AP vs AV")

# Create the plots
p1 <- make_MA_plot(res_AP_vs_AV, title = "AP vs AV (no shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p2 <- make_MA_plot(resLFCnormal_AP_vs_AV, title = "AP vs AV (normal shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)
p3 <- make_MA_plot(resLFCashr_AP_vs_AV, title = "AP vs AV (ashr shrinkage)", lfc_cutoff = 0.3, pval_cutoff = 0.1)

# Combine side-by-side
options(repr.plot.width = 18, repr.plot.height = 6)

# Combine side-by-side
plot_grid(p1, p2, p3, labels = c("A", "B", "C"), ncol = 3, align = 'h')

[1] "MA plots:"
[1] "AP vs AV"

print("plotting dispersion")

# Combine side-by-side
options(repr.plot.width = 6, repr.plot.height = 6)

plotDispEsts(dds)
dispersionFunction(dds)

[1] "plotting dispersion"

structure(function (q) 
coefs[1] + coefs[2]/q, coefficients = c(asymptDisp = 0.0241801588503249, 
extraPois = 2.70666357215647), fitType = "parametric", varLogDispEsts = 0.898737824332759, dispPriorVar = 0.25)

print("RLD and VST transformations")

# Effects of transformations on the variance
rld <- rlog(dds, blind = FALSE)  
vsd <- vst(dds, blind = FALSE) 
ntd <- normTransform(dds)
# meanSdPlot(assay(ntd))
# meanSdPlot(assay(rld))
# meanSdPlot(assay(vsd))

[1] "RLD and VST transformations"

library("pheatmap")

# Select the top 20 differentially expressed genes based on adjusted p-value
top_genes <- rownames(res_AP_vs_AC)[order(res_AP_vs_AC$padj, na.last=NA)][1:20]  #

# Extract normalized transformed counts for the top genes
top_counts <- assay(vsd)[top_genes, ]

# Create annotation dataframe
df <- as.data.frame(colData(dds)["condition"])  # Ensure it is a proper dataframe
colnames(df) <- "Condition"  # Rename column for clarity

# Generate heatmap
options(repr.plot.width = 6, repr.plot.height = 6)
pheatmap(top_counts, 
         cluster_rows=TRUE,  # Cluster rows to group similar genes
         show_rownames=TRUE,  # Show gene names
         cluster_cols=TRUE,  # Cluster samples
         annotation_col=df,  # Add sample condition annotations
         scale="row",  # Normalize each gene's expression across samples
         fontsize_row=8)  # Adjust row text size for readability

print("PCA and MDS plots of rlog- and vst-transformed counts")

[1] "PCA and MDS plots of rlog- and vst-transformed counts"

# 1. rlog transformation and PCA
rld <- rlog(dds, blind = FALSE)
pca_rld <- plotPCA(rld, intgroup = "condition", returnData = TRUE)
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

pca_rld_plot <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  xlab(paste0("PC1: ", percentVar_rld[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_rld[2], "% variance")) +
  ggtitle("PCA (rlog)") +
  theme_minimal()

# 2. rlog MDS
dists_rld <- dist(t(assay(rld)))
mds_rld <- cmdscale(as.matrix(dists_rld))
mds_rld_df <- data.frame(MDS1 = mds_rld[,1], MDS2 = mds_rld[,2], condition = col_data$condition)

mds_rld_plot <- ggplot(mds_rld_df, aes(MDS1, MDS2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  ggtitle("MDS (rlog)") +
  theme_minimal()

# 3. vst transformation and PCA
vsd <- vst(dds, blind = FALSE)
pca_vsd <- plotPCA(vsd, intgroup = "condition", returnData = TRUE)
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))

pca_vsd_plot <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  xlab(paste0("PC1: ", percentVar_vsd[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar_vsd[2], "% variance")) +
  ggtitle("PCA (vst)") +
  theme_minimal()

# 4. vst MDS
dists_vsd <- dist(t(assay(vsd)))
mds_vsd <- cmdscale(as.matrix(dists_vsd))
mds_vsd_df <- data.frame(MDS1 = mds_vsd[,1], MDS2 = mds_vsd[,2], condition = col_data$condition)

mds_vsd_plot <- ggplot(mds_vsd_df, aes(MDS1, MDS2, color = condition)) +
  geom_point(size = 4) +
  scale_color_brewer(palette = "Dark2") +
  ggtitle("MDS (vst)") +
  theme_minimal()

# Combine side-by-side
options(repr.plot.width = 12, repr.plot.height = 8)
# Combine all plots in a 2x2 grid
plot_grid(
  pca_rld_plot, mds_rld_plot,
  pca_vsd_plot, mds_vsd_plot,
  labels = c("A", "B", "C", "D"),
  ncol = 2, align = "hv"
)

using ntop=500 top features by variance

using ntop=500 top features by variance

library(pheatmap)
library(RColorBrewer)
library(gridExtra)
library(grid)

# === RLOG Heatmap ===
rlog_matrix <- assay(rld)
sampleDists_rlog <- dist(t(rlog_matrix))
sampleDistMatrix_rlog <- as.matrix(sampleDists_rlog)
colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)

p1 <- pheatmap(sampleDistMatrix_rlog,
               clustering_distance_rows = sampleDists_rlog,
               clustering_distance_cols = sampleDists_rlog,
               col = colors,
               fontsize_row = 10,
               fontsize_col = 10,
               cellwidth = 60,
               cellheight = 60,
               angle_col = 45,
               main = "Sample Distance Heatmap (rlog)",
               silent = TRUE)

# === VST Heatmap ===
vsd_matrix <- assay(vsd)
sampleDists_vsd <- dist(t(vsd_matrix))
sampleDistMatrix_vsd <- as.matrix(sampleDists_vsd)

p2 <- pheatmap(sampleDistMatrix_vsd,
               clustering_distance_rows = sampleDists_vsd,
               clustering_distance_cols = sampleDists_vsd,
               col = colors,
               fontsize_row = 10,
               fontsize_col = 10,
               cellwidth = 60,
               cellheight = 60,
               angle_col = 45,
               main = "Sample Distance Heatmap (vst)",
               silent = TRUE)

# === Combine with spacing and ensure layout fits ===
grid.newpage()  # Ensures fresh drawing surface

# Combine side-by-side
options(repr.plot.width = 20, repr.plot.height = 10)

# === Convert pheatmap outputs to grobs ===
grob1 <- p1[[4]]
grob2 <- p2[[4]]

# === Combine with cowplot ===
cowplot::plot_grid(grob1, grob2, ncol = 2, rel_widths = c(1, 1))

Attaching package: 'gridExtra'


The following object is masked from 'package:dplyr':

    combine


The following object is masked from 'package:Biobase':

    combine


The following object is masked from 'package:BiocGenerics':

    combine

print("Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances")
# Another option for calculating sample distances is to use the Poisson Distance (Witten 2011), implemented in the PoiClaClu package.
# This measure of dissimilarity between counts also takes the inherent variance structure of counts into consideration when calculating
# the distances between samples. The PoissonDistance function takes the original count matrix (not normalized) with samples as rows
# instead of columns, so we need to transpose the counts in dds.

[1] "Methods to use : GLM-PCA for PCA and PoissonDistance to calculate the sample distances"

print("PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale().")

# === GLM-PCA plot ===
gpca <- glmpca(assay(dds), L = 2)
gpca.dat <- gpca$factors
gpca.dat$sample <- colnames(dds)
gpca.dat$condition <- colData(dds)$condition

p_gpca <- ggplot(gpca.dat, aes(x = dim1, y = dim2, color = condition)) +
  geom_point(size = 4.5, alpha = 0.85, stroke = 1) +
  geom_text_repel(aes(label = sample), size = 4, box.padding = 0.4, max.overlaps = 8) +
  coord_fixed() +
  theme_minimal(base_size = 16) +
  labs(title = "GLM-PCA", x = "GLM-PC1", y = "GLM-PC2") +
  scale_color_brewer(palette = "Set2") +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold", size = 16)
  )

# === Poisson distance heatmap ===
poisd <- PoissonDistance(t(counts(dds)))
samplePoisDistMatrix <- as.matrix(poisd$dd)

sample_names <- colnames(dds)
rownames(samplePoisDistMatrix) <- sample_names
colnames(samplePoisDistMatrix) <- sample_names

colors <- colorRampPalette(rev(brewer.pal(9, "Blues")))(255)

pheat <- pheatmap(samplePoisDistMatrix,
                  clustering_distance_rows = poisd$dd,
                  clustering_distance_cols = poisd$dd,
                  col = colors,
                  fontsize_row = 10,
                  fontsize_col = 10,
                  cellwidth = 50,
                  cellheight = 50,
                  angle_col = 45,
                  main = "Poisson Distance Heatmap",
                  silent = TRUE)

# Convert to grob for use with cowplot
g_poisson <- ggdraw(grobTree(pheat$gtable)) + theme(plot.margin = margin(5, 5, 5, 5))

# Set display size
options(repr.plot.width = 16, repr.plot.height = 8)

# Combine plots with cleaner spacing
combined_plot <- plot_grid(
  g_poisson, p_gpca,
  labels = c("A", "B"),
  label_size = 16,
  nrow = 1,
  rel_widths = c(1.2, 1)
)

# Print
print(combined_plot)

[1] "PCA by using GLMPCA library. RLOG and VSD transformations are more suitable than scale()."

print("Performing Surrogate Variable Analysis")
print("SVA analysis")
# SV1, SV2, ... are surrogate variables — latent (hidden) factors estimated from the data that capture unwanted variation 
# (like batch effects, technical noise, or hidden biological subtypes).
# You can think of them as "virtual covariates" — constructed purely from the structure of your data — 
# that explain sources of variation not included in your model (like treatment or condition).

[1] "Performing Surrogate Variable Analysis"
[1] "SVA analysis"

# === Prepare data ===
dat  <- counts(dds, normalized = TRUE)
idx  <- rowMeans(dat) > 1
dat  <- dat[idx, ]

mod  <- model.matrix(~ condition, colData(dds))
mod0 <- model.matrix(~ 1, colData(dds))

svseq <- svaseq(dat, mod, mod0, n.sv = 2)
head(svseq$sv, 2)

# Set layout: 1 row, 2 columns
par(
  mfrow = c(1, 2),
  mar = c(5, 5, 4, 2) + 0.1,  # bottom, left, top, right margins
  cex.main = 1.4,             # title size
  cex.axis = 1.1,             # axis label size
  cex.lab = 1.2,              # axis title size
  las = 1                     # y-axis labels horizontal
)

# Loop through SV1 and SV2
for (i in 1:2) {
  stripchart(
    svseq$sv[, i] ~ dds$condition,
    vertical = TRUE,
    method = "jitter",
    pch = 21,
    bg = "steelblue",
    col = "black",
    frame.plot = FALSE,
    ylim = c(-0.8, 0.8),  # fixed y-axis range
    main = paste0("Surrogate Variable SV", i),
    ylab = "Surrogate Variable Value",
    xlab = "Condition",
    cex = 1.3
  )
  abline(h = 0, lty = 2, col = "gray50", lwd = 1.5)
}

Number of significant surrogate variables is:  2 
Iteration (out of 5 ):1  2  3  4  5

# Finally, in order to use SVA to remove any effect on the counts from our surrogate variables, we simply add these two surrogate variables 
# as columns to the DESeqDataSet and then add them to the design:

ddssva <- dds
ddssva$SV1 <- svseq$sv[,1]
ddssva$SV2 <- svseq$sv[,2]
design(ddssva) <- ~ SV1 + SV2 + condition
  
ddssva$SV1
ddssva$SV2

# length(ddssva$SV1)
# length(ddssva$SV2)

ddssva <- DESeq(ddssva)
resultsNames(ddssva)

# rowRanges(ddssva)
# colData(ddssva)
# assays(ddssva)
# assay(ddssva)
# length(rowRanges(ddssva))

res_ddssva <- results(ddssva)
resultsNames(res_ddssva)

# Get results for different comparisons
res_ddssva_AP_vs_AC <- results(ddssva, contrast = c("condition", "AP", "AC"))
res_ddssva_AP_vs_AV <- results(ddssva, contrast = c("condition", "AP", "AV"))
res_ddssva_AC_vs_AV <- results(ddssva, contrast = c("condition", "AC", "AV"))

# summary(res_ddssva_AP_vs_AV)
# summary(res_ddssva_AC_vs_AV)
# summary(res_ddssva_AP_vs_AC)

# Save results
write.csv(as.data.frame(res_ddssva_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.sva.csv")
write.csv(as.data.frame(res_ddssva_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.sva.csv")
write.csv(as.data.frame(res_ddssva_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.sva.csv")

###########################################################
###########################################################

print("number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AP_vs_AC, pvalue < 0.05))
dim(subset(res_ddssva_AP_vs_AC, padj < 0.1))

print("number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AP_vs_AV, pvalue < 0.05))
dim(subset(res_ddssva_AP_vs_AV, padj < 0.1))

print("number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1")
dim(subset(res_ddssva_AC_vs_AV, pvalue < 0.05))
dim(subset(res_ddssva_AC_vs_AV, padj < 0.1))

using pre-existing size factors

estimating dispersions

found already estimated dispersions, replacing these

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

[1] "number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1"

[1] "number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1"

# Transform count data
vsd2 <- vst(ddssva, blind = TRUE)
rld2 <- rlog(ddssva, blind = TRUE)

# Get PCA data
pca_vsd <- plotPCA(vsd2, intgroup = "condition", returnData = TRUE)
pca_rld <- plotPCA(rld2, intgroup = "condition", returnData = TRUE)

# Variance explained
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

# PCA plot for VST
p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after SVA (VST)",
    x = paste0("PC1 (", percentVar_vsd[1], "%)"),
    y = paste0("PC2 (", percentVar_vsd[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# PCA plot for RLOG
p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after SVA (RLOG)",
    x = paste0("PC1 (", percentVar_rld[1], "%)"),
    y = paste0("PC2 (", percentVar_rld[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# Show both plots side by side with legends
options(repr.plot.width = 14, repr.plot.height = 6)
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)

using ntop=500 top features by variance

using ntop=500 top features by variance

print("RUVseq analysis")

[1] "RUVseq analysis"

library(RUVSeq)
library(DESeq2)

# Create SeqExpressionSet from DESeq2 object
set <- newSeqExpressionSet(counts(dds))

# Keep genes with sufficient expression
idx  <- rowSums(counts(set) > 5) >= 2
set  <- set[idx, ]

# Normalize
set <- betweenLaneNormalization(set, which = "upper")

# Run DESeq2 just to get raw p-values for empirical control genes
dds_temp <- dds[idx, ]
dds_temp <- DESeq(dds_temp)
res_temp <- results(dds_temp)

# Define empirical control genes as those with high p-value (non-DE)
not.sig <- rownames(res_temp)[which(res_temp$pvalue > 0.1)]
empirical <- rownames(set)[rownames(set) %in% not.sig]

# Apply RUVg with k=2 unwanted factors
set <- RUVg(set, empirical, k = 2)

# Add unwanted factors to DESeq2 design
ddsruv <- dds[idx, ]  # use filtered genes
ddsruv$W1 <- set$W_1
ddsruv$W2 <- set$W_2
design(ddsruv) <- ~ W1 + W2 + condition

# Run DESeq2 with adjusted design
ddsruv <- DESeq(ddsruv)

# Check model variables
resultsNames(ddsruv)

# Get results for different comparisons
res_ddsruv_AP_vs_AC <- results(ddsruv, contrast = c("condition", "AP", "AC"))
res_ddsruv_AP_vs_AV <- results(ddsruv, contrast = c("condition", "AP", "AV"))
res_ddsruv_AC_vs_AV <- results(ddsruv, contrast = c("condition", "AC", "AV"))

# Save results
write.csv(as.data.frame(res_ddsruv_AP_vs_AC), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AC_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_AP_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AP_vs_AV_results.ruv.csv")
write.csv(as.data.frame(res_ddsruv_AC_vs_AV), file = "A549.peaks.IP.samples.DESeq2_AC_vs_AV_results.ruv.csv")

# Summary statistics
print("number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AP_vs_AC, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AP_vs_AC, padj < 0.1)))

print("number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AP_vs_AV, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AP_vs_AV, padj < 0.1)))

print("number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1")
print(dim(subset(res_ddsruv_AC_vs_AV, pvalue < 0.05)))
print(dim(subset(res_ddsruv_AC_vs_AV, padj < 0.1)))

using pre-existing size factors

estimating dispersions

found already estimated dispersions, replacing these

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

using pre-existing size factors

estimating dispersions

found already estimated dispersions, replacing these

gene-wise dispersion estimates

mean-dispersion relationship

final dispersion estimates

fitting model and testing

[1] "number of differentially bound and expressed transcripts : AP vs AC : pvalue < 0.05, and padj < 0.1"
[1] 488   6
[1] 76  6
[1] "number of differentially bound and expressed transcripts : AP vs AV : pvalue < 0.05, and padj < 0.1"
[1] 433   6
[1] 83  6
[1] "number of differentially bound and expressed transcripts : AC vs AV : pvalue < 0.05, and padj < 0.1"
[1] 253   6
[1] 33  6

# Set layout to 1 row, 2 columns (side by side)
par(
  mfrow = c(1, 2),
  mar = c(4, 4, 3, 1),   # margins: bottom, left, top, right
  cex.main = 1.2,        # title size
  cex.axis = 1.0,        # axis tick label size
  cex.lab = 1.1,         # axis title size
  las = 1                # horizontal y-axis labels
)

# Loop over W1 and W2
for (i in 1:2) {
  stripchart(
    pData(set)[, i] ~ dds$condition,
    vertical = TRUE,
    method = "jitter",
    pch = 21,
    bg = "steelblue",
    col = "black",
    frame.plot = FALSE,
    main = paste("W", i),
    ylab = "Factor Value",
    xlab = "Condition",
    cex = 1.1
  )
  abline(h = 0, lty = 2, col = "gray60", lwd = 1)
}

# Transform count data from ddsruv
vsd3 <- vst(ddsruv, blind = TRUE)
rld3 <- rlog(ddsruv, blind = TRUE)

# Get PCA data
pca_vsd <- plotPCA(vsd3, intgroup = "condition", returnData = TRUE)
pca_rld <- plotPCA(rld3, intgroup = "condition", returnData = TRUE)

# Variance explained
percentVar_vsd <- round(100 * attr(pca_vsd, "percentVar"))
percentVar_rld <- round(100 * attr(pca_rld, "percentVar"))

# PCA plot for VST
p1 <- ggplot(pca_vsd, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after RUV (VST)",
    x = paste0("PC1 (", percentVar_vsd[1], "%)"),
    y = paste0("PC2 (", percentVar_vsd[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# PCA plot for RLOG
p2 <- ggplot(pca_rld, aes(PC1, PC2, color = condition)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "PCA after RUV (RLOG)",
    x = paste0("PC1 (", percentVar_rld[1], "%)"),
    y = paste0("PC2 (", percentVar_rld[2], "%)")
  ) +
  theme_minimal(base_size = 14) +
  scale_color_brewer(palette = "Set2") +
  theme(legend.position = "right")

# Show both plots side by side with legends
options(repr.plot.width = 14, repr.plot.height = 6)
plot_grid(p1, p2, labels = c("A", "B"), ncol = 2)

using ntop=500 top features by variance

using ntop=500 top features by variance

library(EnhancedVolcano)

# Color	Label in legend	Meaning
# Grey	NS	Not Significant – the gene did not pass the p-value or log₂FC thresholds
# Green	Log₂ FC	The gene passed the log₂ fold change cutoff but not the p-value cutoff
# Blue	p-value	The gene passed the p-value cutoff but not the log₂FC cutoff
# Red	p value and log₂ FC	The gene passed both p-value and log₂FC thresholds — most interesting hits

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5

# Set up the plotting window size for a more compact layout
options(repr.plot.width = 10, repr.plot.height = 8)

EnhancedVolcano(res_AP_vs_AC,
                lab = rownames(res_AP_vs_AC),
                x = 'log2FoldChange',
                y = 'pvalue',
                pCutoff = pval_cutoff,
                FCcutoff = lfc_cutoff,
                title = 'AP vs AC',
                pointSize = 2.0,
                labSize = 3.0,
                legendPosition = 'right',
                legendLabSize = 10,
                legendIconSize = 3.0,
                drawConnectors = TRUE,
                widthConnectors = 0.5,
                boxedLabels = FALSE)

Warning message:
"ggrepel: 630 unlabeled data points (too many overlaps). Consider increasing max.overlaps"

library(clusterProfiler)
library(org.Hs.eg.db) 
library(GO.db)         
library(DO.db)         
library(KEGGREST)      
library(ReactomePA)    
library(enrichplot)    
library(dplyr)
library(msigdbr)
library(msigdb)
library(msigdf)
library(msigdbdf)

clusterProfiler v4.14.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan,
X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal
enrichment tool for interpreting omics data. The Innovation. 2021,
2(3):100141


Attaching package: 'clusterProfiler'


The following object is masked from 'package:AnnotationDbi':

    select


The following object is masked from 'package:XVector':

    slice


The following object is masked from 'package:purrr':

    simplify


The following object is masked from 'package:IRanges':

    slice


The following object is masked from 'package:S4Vectors':

    rename


The following object is masked from 'package:stats':

    filter


ReactomePA v1.50.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
reactome pathway analysis and visualization. Molecular BioSystems.
2016, 12(2):477-479

enrichplot v1.26.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an
R/Bioconductor package for Disease Ontology Semantic and Enrichment
analysis. Bioinformatics. 2015, 31(4):608-609

# Define thresholds
pval_cutoff <- 0.05
lfc_cutoff <- 0.5
fin_name = "A549.peaks.IP.samples."

res <- res_AP_vs_AC  
head(res,3)

log2 fold change (MLE): condition AP vs AC 
Wald test p-value: condition AP vs AC 
DataFrame with 3 rows and 6 columns
                 baseMean log2FoldChange     lfcSE      stat    pvalue
                <numeric>      <numeric> <numeric> <numeric> <numeric>
ENSG00000310528   11.7191      0.0419129  0.728597 0.0575255  0.954127
ENSG00000310527   54.1219      0.5556707  0.385164 1.4426877  0.149108
ENSG00000225880  658.6745      0.0392962  0.237716 0.1653074  0.868702
                     padj
                <numeric>
ENSG00000310528        NA
ENSG00000310527  0.838676
ENSG00000225880  0.997640

# Filter significant genes
res_sig <- as.data.frame(res) %>%
  rownames_to_column("gene") %>%
  filter(padj < pval_cutoff & abs(log2FoldChange) > lfc_cutoff)

# Map ENSEMBL IDs to Entrez IDs
gene_ids <- bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
res_merge <- merge(res_sig, gene_ids, by.x = "gene", by.y = "ENSEMBL")

# Prepare named gene list for GSEA
gene_list2 <- setNames(res_merge$log2FoldChange, res_merge$ENTREZID)
gene_list2 <- sort(gene_list2, decreasing = TRUE)

head(gene_list2, 2)
length(gene_list2)

'select()' returned 1:1 mapping between keys and columns

Warning message in bitr(res_sig$gene, fromType = "ENSEMBL", toType = "ENTREZID", :
"4.4% of input gene IDs are fail to map..."

library(clusterProfiler)
library(org.Hs.eg.db)
library(cowplot)

# Predefine result objects to avoid "not found" error
result <- NULL
result2 <- NULL

# GO Over-Representation Analysis (ORA)
result <- tryCatch({
  ego <- enrichGO(gene          = gene_ids$ENTREZID,
                  OrgDb         = org.Hs.eg.db,
                  ont           = "BP",
                  keyType       = "ENTREZID",  
                  pAdjustMethod = "BH",
                  pvalueCutoff  = 0.05,
                  qvalueCutoff  = 1,
                  readable      = TRUE)
  
  # Save results
  write.table(ego@result, file = paste0(fin_name, "_GO_OverRepresentation_Results.txt"), 
              row.names = FALSE, col.names = TRUE, quote = FALSE)

  # Save PNG
  png(paste0(fin_name, "_GO_OverRepresentation.png"), width = 1000, height = 800)
  print(dotplot(ego, showCategory = 20, title = "GO ORA (BP)"))
  dev.off()

  # Return ggplot object
  dotplot(ego, showCategory = 20, title = "GO ORA (BP)")

}, error = function(e) {
  cat("Error in GO ORA:", conditionMessage(e), "\n")
  NULL
})


# GO Enrichment Analysis (GSEA)
result2 <- tryCatch({
  ego2 <- gseGO(gene          = gene_list2,
                OrgDb         = org.Hs.eg.db,
                keyType       = "ENTREZID",  
                ont           = "BP",
                pAdjustMethod = "BH",
                pvalueCutoff  = 0.05)

  if (nrow(ego2@result) > 0) {
    write.table(ego2@result, file = paste0(fin_name, "_GO_Enrichment_Results.txt"), 
                row.names = FALSE, col.names = TRUE, quote = FALSE)

    png(paste0(fin_name, "_GO_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)"))
    dev.off()

    dotplot(ego2, showCategory = 20, title = "GO GSEA (BP)")
  } else {
    cat("No enriched terms in GSEA under pvalueCutoff.\n")
    NULL
  }

}, error = function(e) {
  cat("Error in GO GSEA:", conditionMessage(e), "\n")
  NULL
})

# === Display plots side by side if both exist ===
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))
} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)
} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)
} else {
  cat("No enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

no term enriched under specific pvalueCutoff...

No enriched terms in GSEA under pvalueCutoff.

# === KEGG Over-Representation Analysis (ORA) ===
result <- tryCatch({

  kegg_enrich <- enrichKEGG(
    gene           = gene_ids$ENTREZID,
    organism       = "hsa",
    pAdjustMethod  = "BH",
    pvalueCutoff   = 0.05
  )

  if (!is.null(kegg_enrich) && nrow(kegg_enrich@result) > 0) {

    # Save results
    write.table(kegg_enrich@result,
                file = paste0(fin_name, "_KEGG_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save PNG plot
    png(paste0(fin_name, "_KEGG_OverRepresentation_Plot.png"), width = 1000, height = 800)
    print(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))
    dev.off()

    # Return plot object
    return(dotplot(kegg_enrich, showCategory = 20, title = "KEGG ORA"))

  } else {
    cat("⚠️ No enriched KEGG terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in KEGG ORA:", conditionMessage(e), "\n")
  return(NULL)
})



# === KEGG Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  kegg_gse <- gseKEGG(
    geneList      = gene_list2,
    organism      = "hsa",
    minGSSize     = 120,
    pvalueCutoff  = 0.05,
    verbose       = FALSE
  )

  if (!is.null(kegg_gse) && nrow(kegg_gse@result) > 0) {

    # Save results
    write.table(kegg_gse@result,
                file = paste0(fin_name, "_KEGG_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save PNG plot
    png(paste0(fin_name, "_KEGG_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))
    dev.off()

    # Return plot object
    return(dotplot(kegg_gse, showCategory = 20, title = "KEGG GSEA"))

  } else {
    cat("⚠️ No enriched KEGG terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in KEGG GSEA:", conditionMessage(e), "\n")
  return(NULL)
})



# === Display Plots Nicely ===
# Set default figure size
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  # Side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No KEGG enrichment plots to display.\n")
}

Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...

Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...

Warning message in rep(yes, length.out = len):
"'x' is NULL so the result will be NULL"

❌ Error in KEGG ORA: replacement has length zero

no term enriched under specific pvalueCutoff...

⚠️ No enriched KEGG terms found in GSEA.

NULL

⚠️ No KEGG enrichment plots to display.

library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(pathview)         # Optional for WikiPathways
library(dplyr)
library(DOSE)
library(rWikiPathways)

# === WikiPathways Over-Representation Analysis (ORA) ===
result <- tryCatch({

  wikipathways_enrich <- enrichWP(
    gene           = gene_ids$ENTREZID,
    organism       = "Homo sapiens",
    pvalueCutoff   = 0.05
  )

  if (!is.null(wikipathways_enrich) && nrow(wikipathways_enrich@result) > 0) {

    # Save results
    write.table(wikipathways_enrich@result,
                file = paste0(fin_name, "_WikiPathways_ORA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save plot
    png(paste0(fin_name, "_WikiPathways_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))
    dev.off()

    # Return plot object
    return(dotplot(wikipathways_enrich, showCategory = 20, title = "WikiPathways ORA"))

  } else {
    cat("⚠️ No enriched WikiPathways terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in WikiPathways ORA:", conditionMessage(e), "\n")
  return(NULL)
})



# === WikiPathways Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  wikipathways_gse <- gseWP(
    gene          = gene_list2,
    organism      = "Homo sapiens",
    pvalueCutoff  = 0.05
  )

  if (!is.null(wikipathways_gse) && nrow(wikipathways_gse@result) > 0) {

    # Save results
    write.table(wikipathways_gse@result,
                file = paste0(fin_name, "_WikiPathways_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save plot
    png(paste0(fin_name, "_WikiPathways_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))
    dev.off()

    # Return plot object
    return(dotplot(wikipathways_gse, showCategory = 20, title = "WikiPathways GSEA"))

  } else {
    cat("⚠️ No enriched WikiPathways terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in WikiPathways GSEA:", conditionMessage(e), "\n")
  return(NULL)
})



# === Display plots ===
options(repr.plot.width = 5, repr.plot.height = 5)  # Default size

if (!is.null(result) && !is.null(result2)) {
  # Show both plots side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No WikiPathways enrichment plots to display.\n")
}

##############################################################################
Pathview is an open source software package distributed under GNU General
Public License version 3 (GPLv3). Details of GPLv3 is available at
http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
formally cite the original Pathview paper (not just mention it) in publications
or products. For details, do citation("pathview") within R.

The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
license agreement (details at http://www.kegg.jp/kegg/legal.html).
##############################################################################

DOSE v4.0.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an
R/Bioconductor package for Disease Ontology Semantic and Enrichment
analysis. Bioinformatics. 2015, 31(4):608-609


Attaching package: 'rWikiPathways'


The following object is masked from 'package:edgeR':

    getCounts


The following object is masked from 'package:GenomeInfoDb':

    listOrganisms


using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

no term enriched under specific pvalueCutoff...

⚠️ No enriched WikiPathways terms found in GSEA.

NULL

⚠️ No WikiPathways enrichment plots to display.

library(ReactomePA)
library(clusterProfiler)
library(enrichplot)
library(cowplot)

# === Reactome Over-Representation Analysis (ORA) ===
result <- tryCatch({

  reactome_ora <- enrichPathway(
    gene           = gene_ids$ENTREZID,
    organism       = "human",
    pAdjustMethod  = "BH",
    pvalueCutoff   = 0.05
  )

  if (!is.null(reactome_ora) && nrow(reactome_ora@result) > 0) {

    # Save results
    write.table(reactome_ora@result,
                file = paste0(fin_name, "_Reactome_ORA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save plot
    png(paste0(fin_name, "_Reactome_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))
    dev.off()

    # Return plot for screen
    return(dotplot(reactome_ora, showCategory = 20, title = "Reactome ORA"))

  } else {
    cat("⚠️ No enriched Reactome terms found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in Reactome ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Reactome GSEA Analysis ===
result2 <- tryCatch({

  reactome_gsea <- gsePathway(
    gene            = gene_list2,
    organism        = "human",
    pAdjustMethod   = "BH",
    pvalueCutoff    = 0.05
  )

  if (!is.null(reactome_gsea) && nrow(reactome_gsea@result) > 0) {

    # Save results
    write.table(reactome_gsea@result,
                file = paste0(fin_name, "_Reactome_Enrichment_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    # Save plot
    png(paste0(fin_name, "_Reactome_Enrichment_Plot.png"), width = 1000, height = 800)
    print(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))
    dev.off()

    # Return plot for screen
    return(dotplot(reactome_gsea, showCategory = 20, title = "Reactome GSEA"))

  } else {
    cat("⚠️ No enriched Reactome terms found in GSEA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in Reactome GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 6, repr.plot.height = 6)

if (!is.null(result) && !is.null(result2)) {
  # Show both side-by-side
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No Reactome enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

no term enriched under specific pvalueCutoff...

⚠️ No enriched Reactome terms found in GSEA.

NULL

⚠️ No Reactome enrichment plots to display.

library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)

# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C2")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)

# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID  # for ORA

# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({

  msig_enrich <- enricher(
    gene       = gene_list,
    TERM2GENE  = C2_t2g
  )

  if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {

    write.table(msig_enrich@result,
                file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
    dev.off()

    return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))

  } else {
    cat("⚠️ No significant MSigDB pathways found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  msig_gsea <- GSEA(
    geneList   = gene_list2,
    TERM2GENE  = C2_t2g,
    pvalueCutoff = 0.05
  )

  if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {

    write.table(msig_gsea@result,
                file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
    dev.off()

    return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))

  } else {
    cat("⚠️ No significant MSigDB GSEA pathways found.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 20, repr.plot.height = 20)

if (!is.null(result) && !is.null(result2)) {
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No MSigDB enrichment plots to display.\n")
}

Warning message:
"The `category` argument of `msigdbr()` is deprecated as of msigdbr 9.0.0.
ℹ Please use the `collection` argument instead."
using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

no term enriched under specific pvalueCutoff...

⚠️ No significant MSigDB GSEA pathways found.

NULL

⚠️ No MSigDB enrichment plots to display.

library(msigdbr)
library(clusterProfiler)
library(enrichplot)
library(cowplot)
library(dplyr)

# === Prepare MSigDB C2 gene sets ===
msig_genesets <- msigdbr(species = "Homo sapiens", category = "C5")
C2_t2g <- msig_genesets %>% dplyr::select(gs_name, entrez_gene)

# Use gene_ids$ENTREZID for ORA, and named gene_list2 for GSEA
gene_list <- gene_ids$ENTREZID  # for ORA

# === MSigDB Over-Representation Analysis (ORA) ===
result <- tryCatch({

  msig_enrich <- enricher(
    gene       = gene_list,
    TERM2GENE  = C2_t2g
  )

  if (!is.null(msig_enrich) && nrow(msig_enrich@result) > 0) {

    write.table(msig_enrich@result,
                file = paste0(fin_name, "_MSigDB_OverRepresentation_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_ORA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))
    dev.off()

    return(dotplot(msig_enrich, showCategory = 20, title = "MSigDB ORA"))

  } else {
    cat("⚠️ No significant MSigDB pathways found in ORA.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB ORA:", conditionMessage(e), "\n")
  return(NULL)
})


# === MSigDB Gene Set Enrichment Analysis (GSEA) ===
result2 <- tryCatch({

  msig_gsea <- GSEA(
    geneList   = gene_list2,
    TERM2GENE  = C2_t2g,
    pvalueCutoff = 0.05
  )

  if (!is.null(msig_gsea) && nrow(msig_gsea@result) > 0) {

    write.table(msig_gsea@result,
                file = paste0(fin_name, "_MSigDB_GSEA_Results.txt"),
                sep = "\t", row.names = FALSE, quote = FALSE)

    png(paste0(fin_name, "_MSigDB_GSEA_Plot.png"), width = 1000, height = 800)
    print(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))
    dev.off()

    return(dotplot(msig_gsea, showCategory = 20, title = "MSigDB GSEA"))

  } else {
    cat("⚠️ No significant MSigDB GSEA pathways found.\n")
    return(NULL)
  }

}, error = function(e) {
  cat("❌ Error in MSigDB GSEA:", conditionMessage(e), "\n")
  return(NULL)
})


# === Display plots ===
options(repr.plot.width = 10, repr.plot.height = 10)

if (!is.null(result) && !is.null(result2)) {
  options(repr.plot.width = 12, repr.plot.height = 6)
  plot_grid(result, result2, labels = c("A", "B"), ncol = 2, rel_widths = c(1, 1))

} else if (!is.null(result)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result)

} else if (!is.null(result2)) {
  options(repr.plot.width = 7, repr.plot.height = 6)
  print(result2)

} else {
  cat("⚠️ No MSigDB enrichment plots to display.\n")
}

using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).


preparing geneSet collections...

GSEA analysis...

leading edge analysis...

done...

⚠️ No MSigDB enrichment plots to display.

	Geneid	Chr	Start	End	Strand	Length	AC1_IP.AC1_IP.CLAM.genomicAllAligned.sorted.bam	AC1_NP.AC1_NP.CLAM.genomicAllAligned.sorted.bam	AC2_IP.AC2_IP.CLAM.genomicAllAligned.sorted.bam	AC2_NP.AC2_NP.CLAM.genomicAllAligned.sorted.bam	AP1_IP.AP1_IP.CLAM.genomicAllAligned.sorted.bam	AP1_NP.AP1_NP.CLAM.genomicAllAligned.sorted.bam	AP2_IP.AP2_IP.CLAM.genomicAllAligned.sorted.bam	AP2_NP.AP2_NP.CLAM.genomicAllAligned.sorted.bam	AV1_IP.AV1_IP.CLAM.genomicAllAligned.sorted.bam	AV1_NP.AV1_NP.CLAM.genomicAllAligned.sorted.bam	AV2_IP.AV2_IP.CLAM.genomicAllAligned.sorted.bam	AV2_NP.AV2_NP.CLAM.genomicAllAligned.sorted.bam
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1;chr1	14621;14721;19121;19221	14721;14821;19221;19321	+;+;+;+	402	3	1	0	2	0	0	0	0	2	1	0	0
2	ENSG00000310526	chr1;chr1;chr1;chr1	14656;14756;19156;19256	14756;14856;19256;19356	-;-;-;-	402	0	0	0	2	3	0	0	0	0	1	0	0

	Geneid	Chr	Start	End	Strand	Length	AC1_IP.AC1_IP	AC1_NP.AC1_NP	AC2_IP.AC2_IP	AC2_NP.AC2_NP	AP1_IP.AP1_IP	AP1_NP.AP1_NP	AP2_IP.AP2_IP	AP2_NP.AP2_NP	AV1_IP.AV1_IP	AV1_NP.AV1_NP	AV2_IP.AV2_IP	AV2_NP.AV2_NP
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1;chr1	14621;14721;19121;19221	14721;14821;19221;19321	+;+;+;+	402	3	1	0	2	0	0	0	0	2	1	0	0
2	ENSG00000310526	chr1;chr1;chr1;chr1	14656;14756;19156;19256	14756;14856;19256;19356	-;-;-;-	402	0	0	0	2	3	0	0	0	0	1	0	0

	Geneid	Chr	Start	End	Strand	Length	AC1_IP	AC1_NP	AC2_IP	AC2_NP	AP1_IP	AP1_NP	AP2_IP	AP2_NP	AV1_IP	AV1_NP	AV2_IP	AV2_NP
	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1;chr1;chr1;chr1	14621;14721;19121;19221	14721;14821;19221;19321	+;+;+;+	402	3	1	0	2	0	0	0	0	2	1	0	0
2	ENSG00000310526	chr1;chr1;chr1;chr1	14656;14756;19156;19256	14756;14856;19256;19356	-;-;-;-	402	0	0	0	2	3	0	0	0	0	1	0	0

	Geneid	Chr	Start	End	Strand	Length	AC1_IP	AC1_NP	AC2_IP	AC2_NP	AP1_IP	AP1_NP	AP2_IP	AP2_NP	AV1_IP	AV1_NP	AV2_IP	AV2_NP
	<chr>	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1	14621	19321	+;+;+;+	402	3	1	0	2	0	0	0	0	2	1	0	0
2	ENSG00000310526	chr1	14656	19356	-;-;-;-	402	0	0	0	2	3	0	0	0	0	1	0	0

	Geneid	Chr	Start	End	Strand	Length	AC1_IP	AC1_NP	AC2_IP	AC2_NP	AP1_IP	AP1_NP	AP2_IP	AP2_NP	AV1_IP	AV1_NP	AV2_IP	AV2_NP
	<chr>	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>
1	ENSG00000290825	chr1	14621	19321	+;+;+;+	402	3	1	0	2	0	0	0	0	2	1	0	0
2	ENSG00000310526	chr1	14656	19356	-;-;-;-	402	0	0	0	2	3	0	0	0	0	1	0	0
3	ENSG00000227232	chr1	14696	19296	-;-	202	0	0	0	0	0	0	0	0	0	0	0	0
4	ENSG00000310528	chr1	189386	195386	-;-;-	303	15	11	28	21	21	5	21	7	2	17	5	15
5	ENSG00000310527	chr1	187146	195346	-;-;-;-;-;-;-	707	79	38	86	46	124	16	112	13	13	55	14	38
6	ENSG00000293331	chr1	629954	632454	-;-;-;-;-;-	606	0	0	0	0	0	0	0	0	0	1	0	0
7	ENSG00000225880	chr1	629745	634745	-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-;-	2011	916	344	1057	237	775	161	1236	123	312	971	290	1315
8	ENSG00000225630	chr1	630340	630440	+	101	0	0	0	0	0	0	0	0	1	0	0	0
9	ENSG00000237973	chr1	631274	632474	+;+;+	303	23	8	16	9	19	5	25	1	2	24	5	21
10	ENSG00000278791	chr1	632325	632425	-	101	0	0	1	0	0	0	0	0	2	0	0	1
11	ENSG00000248527	chr1	633696	634196	+;+	202	4004	1313	4455	827	3746	874	4091	509	1094	3920	1347	2164
12	ENSG00000198744	chr1	634476	634676	+;+	201	0	0	0	0	0	0	0	0	0	0	0	0
13	ENSG00000237491	chr1	790239	790339	+	101	3	10	12	5	33	4	1	5	5	14	6	10
14	ENSG00000228794	chr1	827038	856238	+;+;+;+	404	53	112	59	107	65	48	53	29	17	38	14	23
15	ENSG00000188290	chr1	999962	1000062	-	101	64	11	86	21	66	5	91	2	8	53	10	11
16	ENSG00000187608	chr1	1014238	1014338	+	101	170	12	232	17	37	5	48	3	10	148	2	76
17	ENSG00000242590	chr1	1055433	1055533	+	101	450	17	709	22	536	14	865	4	51	278	64	141
18	ENSG00000131591	chr1	1081918	1082918	-;-;-;-;-;-;-;-;-;-	1001	121	84	162	149	121	64	250	30	22	79	38	35

	AC1_IP	AC2_IP	AP1_IP	AP2_IP	AV1_IP	AV2_IP
ENSG00000310528	11.05393	15.72471	14.99586	12.72951	4.732113	11.07866
ENSG00000310527	58.21735	48.29732	88.54695	67.89072	30.758732	31.02024

A matrix: 2 × 2 of type dbl
-0.3879922	0.0142916
-0.2524473	-0.1711288